Method and system for detection of respiratory variation in plethysmographic oximetry

ABSTRACT

A method and system for detection of respiratory variation in pulse oximetry are provided. In one embodiment, the method includes detecting a pattern consistent with a respiratory cycle of a waveform representing cardiac oscillations and identifying an abnormality based on the pattern. A system is further provided including a database and a module. The database to store data representing a physiological condition of a patient over a period of time, wherein the data comprises data corresponding to respiratory activity and data corresponding to cardiac oscillations, wherein the data corresponding to respiratory activity comprises a first waveform and the data corresponding to the cardiac oscillations comprises a second waveform; and a module for detecting a pattern of the second waveform consistent with a cycle of a first waveform and detecting an abnormality in the pattern.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a divisional of co-pending U.S. patent application Ser. No.12/315,813, filed Dec. 5, 2008, which claims priority from U.S.Provisional Patent Application No. 60/992,973, filed on Dec. 6, 2007.

BACKGROUND

1. Field

A method and system for detection of variations in plethysmographicoximetry.

2. Background

Pulsus paradoxus is a term referring to a systolic arterial pressure andpulse pressure that weakens abnormally during inspiration. It was firstrecognized in 1873 when an irregularity of the palpable pulse wasobserved while the heart sounds indicated that the cardiac rhythm wasregular. It was found that the “irregularity” of the pulse resulted froma reduction in left ventricular stroke volume leading to an impalpablepulse during inspiration. Pulsus paradoxus may be symptomatic of variousabnormalities including pericardial tamponade, worsening asthma, chronicobstructive pulmonary disease, congestive heart failure, pulmonaryedema, chronic dyspnea, obstructive sleep apnea and tensionpneumothorax. If left undetected, these disorders may result indeterioration or death of critically ill patients. Thus, early detectionis essential.

In a healthy individual, arterial and venous blood pressure varythroughout the respiratory cycle. It is not uncommon to see an increasein blood pressure during expiration followed by a decrease in bloodpressure during inspiration. Such fluctuations may occur due to theintrathoracic pressure changes generated during breathing. Although theexact physiology behind pulsus paradoxus may vary depending upon thedisease it is symptomatic of, the exaggerated pressure decrease maygenerally be explained by the interrelationship between the changingintrathoracic pressure during the respiratory cycle and the twoventricular chambers of the heart. During inspiration intrathoracicpressure decreases which augments right heart filling, pulmonaryvascular compliance, and right ventricular stroke volume while reducingleft heart filling and output.

Pulsus paradoxus may be detected by monitoring changes in blood pressurethroughout the respiratory cycle. Under normal conditions, an individualmay experience a decrease in arterial blood pressure of less than 10millimeters mercury (mmHg) during inspiration. An abnormality isidentified where this pressure decrease exceeds 10 mmHg. Currently thereare a variety of techniques available for detecting this pressuredecrease during inspiration.

One such technique for detection of pulsus paradoxus requires graduallydeflating a sphygmomanometer (blood pressure cuff) while listening forthe onset of Korotkoff sounds (sounds resulting from arterial pressurewaves passing through the occluding cuff) during normal respiration. TheKorotkoff sounds will first be audible during expiration only, and afterfurther deflation of the cuff, during inspiration as well. If the cuffis deflated more than 10 mmHg between detection of intermittent andconstant Korotkoff sounds, pulsus paradoxus is said to be present. Thismethod of detection is problematic for a number of reasons, not theleast of which is that automated blood pressure monitoring recording isnow standard throughout the health care industry and this technique isincapable of detecting respiratory fluctuations in arterial pressuresince only one set of systolic and diastolic pressures are recorded.Even when manual sphygmomanometers are available, the operator has to bealerted to the possibility that pulsus paradoxus could be present andknow how to perform the test. In addition, blood pressure values aresubjective in that they are reliant upon the operator's ability tolisten to the sounds while watching the fluctuations of the gauge. Theonly record is therefore the operators' hand-written description of ahighly subjective test.

A second technique used in detecting pulsus paradoxus is by directmonitoring of arterial pressure with an indwelling intra-arterialcatheter. This technique is more accurate than sphygmomanometry indetecting pulsus paradoxus because it results in a permanent recordingof the arterial waveform and pressure and thus allows for an objectivemeasurement. Due to its invasive nature, however, it is often painful tothe patient and requires a highly trained health care provider.

Infrared photosensors used for pulse oximetry and plethysmography may beutilized for a third technique that may be used for detecting pulsusparadoxus. In this technique, changes in the intensity of an infrared(IR) beam passing through a patient's finger tip, toe, or earlobe areobtained to measure fluctuations in regional blood volume, a correlateof blood pressure. A clip-on probe sends an IR beam through the fleshytissue and receives reflections therefrom. Changes in the amount ofblood in the measurement area (i.e., a capillary bed) cause changes inabsorption or variation, and such changes vary along with the amount ofblood delivered to that tissue. Thus, although not a direct measure ofthe patient's blood pressure, the plethysmographic signal emulates thewaveform contour and magnitude of direct intra-arterial pulse pressureand is typically displayed on a monitor screen along with theelectrocardiogram and respiratory excursions. Clinical use of thismeasurement, called plethysmographic oximetry (PO), has been reported todetect pulsus paradoxus in children with pericardial tamponade and inadults with respiratory distress from obstructive lung disease but theseresults have not yet been incorporated into devices that would makepossible simply applied, non-invasive, real-time detection of pulsusparadoxus.

The above described techniques for detection of pulsus paradoxus arehighly subjective, labor intensive and inaccurate (if measured with ablood pressure cuff), or invasive (if done with arterial lines) andtherefore, are rarely used. Thus, the ability to detect the ominouscondition of pulsus paradoxus requires a high level of suspicion andcumbersome or invasive technology. As a result, pulsus paradoxus andtherefore early signs of several life-threatening conditions often goundetected.

BRIEF DESCRIPTION OF THE DRAWINGS

The following illustration is by way of example and not by way oflimitation in the figures of the accompanying drawings in which likereferences indicate like elements. It should be noted that references to“an” or “one” embodiment in this disclosure are not necessarily to thesame embodiment, and such references mean at least one.

FIG. 1A illustrates simultaneous recordings from a patient of anelectrocardiogram wave review (ECG), a blood pressure wave review (ABP),a plethysmographic wave review (PLETH) and a respiratory wave review(RESP).

FIG. 1B illustrates the simultaneous recordings of FIG. 1A.

FIG. 2 illustrates a physiologic respiratory wave review of a patient.

FIG. 3 illustrates a pathological wave review of a patient indicative ofpulsus paradoxus.

FIG. 4 illustrates a pathological wave review of a patient indicative ofchanges in stroke volume resulting from premature atrial beats.

FIG. 5 illustrates a pathological wave review of the patent of FIG. 4indicative of wide complex tachycardia.

FIG. 6 illustrates a system for detecting pulsus paradoxus.

DETAILED DESCRIPTION

Automatic and sensitive detection of respiratory variation in cardiacactivity may be a major asset in emergency departments, medical andsurgical intensive care units (ICU), operating rooms, and hospices sinceit is especially difficult to determine worsening of many conditionswith current manual systems and those focusing on changes in arterialblood pressure alone. Such detection may allow health care physicians tomore quickly address and treat conditions such as, for example, chronicapnea and tension pneumothorax. Moreover, an automated system and methodas described herein may be useful in nursing homes or hospices wheredetection of impending respiratory failure may allow families to benotified of important changes in conditions as they occur. Stillfurther, detection of hemodynamic changes that happen consistently inobstructive sleep apnea may improve diagnosis or help avoid expensivediagnostic testing in sleep laboratories. It is thus believed, automateddetection of pulsus paradoxus would help to identify and intervene inpatient care resulting in thousands of lives saved each year.

In one embodiment, a method for detection of pulsus paradoxus isdisclosed. In one aspect, the method includes detection of a patternconsistent with a respiratory cycle of waveforms representing cardiacoscillations to detect an abnormality in the pattern. In one embodiment,a plethysmographic oximeter system may be used to measure and display arespiratory waveform and concurrent waveform representing cardiacoscillations (plethysmograhic oximetry waveform) over a period of time.In this context, a lead from the system may include sensors formonitoring a patient's respiratory activity. The system may furtherinclude a lead for monitoring cardiac activities. Still further, leadsmay include electrocardiogram sensors (ECG) and/or sensors for detectinga pulse rate. The system may display information relating to each ofthese measurements in a waveform on an interface of the system. Thesewaveforms may then be analyzed and scored by dividing averaged offsetsof pulse waves from a base line with average wave amplitudes over arespiratory cycle. A score falling within a particular range indicatesan abnormality (e.g. oscillating base of a plethysmographic wave from abaseline).

FIG. 1A illustrates simultaneous normal ECG 102, ABP 104, PLETH 106 andRESP 108 recordings from a patient. ECG 102, ABP 104, PLETH 106 and RESP108 include a tracing of consecutive cycles over a period of sevenseconds. Components of the recordings illustrated in FIG. 1A will bedescribed in more detail in reference to FIG. 1B.

An electrocardiogram (ECG) records electrical potentials from the heart.Under normal conditions, an electrical stimulus is generated by thesinus node located in the right atrium of the heart. The sinus node isthe pacemaker of the heart and begins the process of depolarization ofthe atrium and ventricle through conduction pathways and generates anelectrical stimulus which initiates the heart beat (e.g., 60 to 190times per minute depending on the age of the patient). The electricalimpulse travels from the sinus node to the atria ventricular (AV) nodewhere it stops for a brief period and continues down the conductionpathways via the bundle of His into the ventricles. Accordingly, theright and left atria are depolarized first followed by depolarization ofthe right and left ventricle. As the electrical impulse moves throughthe conduction system, the heart contracts. Each contraction representsone heart beat.

In FIG. 1B, the large vertical spike of ECG 102 is R wave 114 whichrepresents depolarization of ventricles. Each R wave 114 is followed bycontraction and pumping of blood. R wave 114 is used as a signal fromwhich other signals are identified. The lower amplitude spike followingR wave 114 is T wave 120. T wave 120 represents repolarization ofcardiac muscle which then prepares for the next signal from the sinusnode.

Each ventricular contraction produces an arterial waveform ABP 104 whichcan be seen to follow R wave 114 due to the time delay followingdepolarization and the time for blood to get to the peripheral recordingsite. ABP 104 is a recording of the arterial pressure. The recordingutilizes a cannula inserted into an artery which is in turn transducedinto a scaled electrical signal. The height or amplitude is scaled inmmHg. ABP 104 illustrates the ABP in the radial artery caused by thepressure generated by contraction of the heart's left ventricle. ABPincludes a systolic pressure defined as the peak pressure in thearteries during the cardiac cycle and a diastolic pressure defined asthe lowest pressure at the resting phase of the cardiac cycle. ABPwaveform 104 includes “up” 122 and “down” 124 fluctuations of thearterial blood pressure resulting from the pulsatile nature of thecardiac output. The pulse pressure is determined by the interaction ofthe stroke volume versus the volume and elasticity of the majorarteries. Normal ABP ranges for adult humans are systolic between 90mmHg and 135 mmHg and diastolic between 50 mmHg and 90 mmHg.

Referring to PLETH wave review 106, the waveform represents a patienthaving a steady unchanging pulse with a heart rate of about 80 beats perminute. PLETH waveform 106 may be recorded from a fingertip deviceutilized to determine an index of tissue oxygenation. In this aspect,each wave represents a pressure exerted when the heart contracts. For atypical healthy adult human a normal PLETH waveform 106 is about 120mmHg/ 60 mmHg. Horizontal borderlines 130, 132 are illustrated alongwaveform 106 to emphasize the regularity of the waves. In particular,peak borderline 130 extends along an upper limit defined by wave peaks126 of PLETH waveform 106 and baseline 132 extends along a lower limitalong wave bases 128 of PLETH waveform 106. It can be seen that thewaves of PLETH waveform 106 have a regular height or amplitude (i.e.,distance from peak 126 to baseline 132). In addition, each wave base 128is substantially aligned with baseline 132 such that no significantbaseline oscillations are present.

Referring to RESP wave review 108, the waveform represents respiratoryexcursions where “up” waves 134, 138, 142 represent inspiration and“down” waves 136, 140 represent expiration. RESP review 108 may beobtained from impedance plethysmography using standard ECG electrodes.Impedance plethysmography is a non-quantitative signal arising frommovement of ECG sensors placed on the patient's thorax as the thoraxrises and falls. RESP waveform 108 is shown having a regular respiratorytracing and may have a rate of approximately 16 to 30 breaths perminute. Wave peaks representing inspiration may be used to define eachrespiratory cycle. For example, a respiratory cycle may be defined bypeaks 134 and 138 while another cycle may be defined by peaks 138 and142.

It can be seen from FIGS. 1A and 1B that ECG 102, ABP 104 and PLETH 106waveforms have the same frequency though it is noted that there is aphysiologic offset such that the ABP signal occurs after ECG and thePLETH signal occurs after ABP. In a normal healthy human, there is acorrelation in the ECG, ABP and PLETH waveforms and at most a subtlerelationship between PLETH and RESP.

FIG. 2 illustrates a physiologic respiratory wave review of a patient.RESP waveform 200 shows an exemplary embodiment of a physiologic wavereview for purposes of illustrating a measurement of an averagerespiratory cycle distance for use in detection of an abnormality in apattern representing cardiac oscillations. In this aspect, a waveform204 of RESP 200 includes peaks 212, 214, 216, 218 representinginspiration. A baseline 202 from which an amplitude of peaks 212, 214,216, 218 may be determined is further shown. An average respiratorycycle distance may be determined by first measuring a distance from onepeak to the next. An average distance, D, may then be determined byaveraging the distances of at least two consecutive respiratory cycles.For example, a distance, d¹, between peaks 212 and 214 may be measuredto identify a first cycle 206 while a distance d² between peaks 214 and216 may be measured to identify a second cycle 208. An average is takenof distance d¹ and d² to determine average respiratory cycle distance D.Still further, distance d³ (between peaks 216 and 218) of a thirdrespiratory cycle 210 may be measured and averaged with distances d¹ andd² to arrive at average respiratory cycle distance D.

FIG. 3 illustrates a wave review with variation in a PLETH waveform fora patient with a regular sinus rhythm. In particular, it can be seenfrom FIG. 3 that bases of waves of PLETH 300 are offset (i.e. oscillate)from a baseline 305. These oscillations may be analyzed to determinewhether they are indicative of pulsus paradoxus by dividing the wavesinto groups, or cycles, which correspond to an average respiratory cycledistance, D, for the patient as described above. For the purpose ofillustrating this concept, an average respiratory cycle D (e.g. averagedistance of cycles 206, 208, 210) as described above in reference toFIG. 2 is used to identify wave cycles 320, 330 of FIG. 3. It isrecognized, however, that RESP waveform 200 and PLETH waveform 300 arenot drawn to scale. For example, cycle 320 is identified by measuringaverage respiratory cycle D from the beginning of a blood pressureincrease along baseline 305. Cycle 330 then extends a distance D from anend of cycle 320. It can be seen from FIG. 3 that each cycle 320, 330includes about eight wave peaks 350 and thus approximately eight waves.Once a cycle is determined, a height, h, is measured from each wave base340 to baseline 305. An average height, H, is then determined using eachheight, h, measured from wave bases 340 to baseline 305. For example, incycle 320 an average height H may be calculated by adding each ofheights h¹, h², h³, h⁴, h⁵, h⁶, h⁷, h⁸ together and dividing by eight.Average height H is then used to form a triangle as shown in FIG. 3having a height H, a base D and hypotenuse 360 representative of abest-fit line to wave bases 340. An average wave amplitude, A, may thenbe determined for each cycle 320, 330 by determining an amplitude, a, ofeach wave from hypotenuse 360 to a wave peak 350 and averaging theamplitudes. For example, in cycle 320 an average amplitude A may becalculated by adding each amplitude a¹, a², a³, a⁴, a⁵, a⁶, a⁷, a⁸ fromhypotenuse 360 to a wave peak 350 and averaging the amplitudes. Theabove described analysis may be repeated for cycle 330. A value (i.e.,score) for cycles 320 and 330 may then be determined according to theformula H/A wherein H represents the average offset from the baseline ofthe waves and A represents average amplitude for cycle. Using the aboveanalysis, the degree of wave oscillations within each cycle may beevaluated to determine whether they are indicative of pulsus paradoxus.

For example, in one embodiment illustrated in FIG. 3, average distance,D, may be approximately 3.5 centimeters (cm) such that cycle 320 is madeup of waves within a region of baseline 305 3.5 cm in length. An averageheight, H, of cycle 320 may be approximately 0.356 cm (i.e., h¹=0.5 cm,h²=0.5 cm, h³=0.4 cm, h⁴=0.3 cm, h⁵=0.3 cm, h⁶=0.3 cm, h⁷=0.25 cm,h⁸=0.3 cm) and average amplitude A may be approximately 1.113 cm (i.e.,a¹=0.7 cm, a²=1.1 cm, a³=1.3 cm, a⁴=1.35 cm, a⁵=1.25 cm, a⁶=1.2 cm,a⁷=1.1 cm, a⁸=0.9 cm) resulting in a score of 0.32 (0.356 cm/ 1.113 cm).In one embodiment, an alarm 560 (See FIG. 5) may be triggered by a score(calculated, for example, as described above, offset over amplitude)falling within a range indicative of pulsus paradoxus (i.e., pressuredecrease greater than 10 mmHg). Thus, in one embodiment a valueindicative of pulsus paradoxus may be 0.32. In one aspect, a rangeindicative of pulsus paradoxus may be, for example, a score from about0.3 to about 2.5 and still further a score from about 1.6 to about 2.5may be indicative of pulsus paradoxus. It is believed a score below 0.3is within the normal range. Still further, alarm 560 may be triggered bya score indicative of a variety of cardiopulmonary disorders, often incombination. In this aspect, the alarm may be triggered by a score in arange of, for example, about 0.3 to about 1.6.

As described herein, variations in a plethysmographic signal aspreviously described may indicate pulsus paradoxus. It is furthercontemplated, however, that each of the wave reviews illustrated inFIGS. 1A and 1B (ECG, ABP, RESP), should be considered in determiningwhether or not variations in a patient's plethysmographic signal areindicative of pulsus paradoxus or some other condition. For example,where plethysmographic signal variations are detected in a patient withan irregular sinus rhythm, the variations in the plethysmographic signalmay correspond to the irregular sinus rhythm thereby suggesting anarrhythmic, rather than respiratory related, reason for the variations.

FIG. 4 illustrates one such pathological wave review of a patientindicative of changes in stroke volume resulting from premature atrialbeats. The term “stroke volume” as used herein refers to the volume ofblood ejected from a ventricle with each beat of the heart. FIG. 4 showswave review 400 including an ECG waveform 410 and PLETH waveform 420 ofa patient. In this embodiment, an abnormality is detected based upon thefrequency of ECG 410 complexes with respect to PLETH 420 over severalconsecutive cycles 430, 440, 450. In particular, in review 400, PLETH420 illustrates three repetitive plethysmographic waves 460, 470, 480corresponding to cycles 430, 440 and 450 of ECG waveform 410. Waves 460,470, 480 represent a late beat 460 following a pause, a normal beat 470and a premature beat 480. In particular, beat 460 is seen having a largeamplitude due to the extra fill time resulting from a pause preceedingthe beat. In the case of premature beat 480, the resulting pulse has amuch lower amplitude than normal because the heart does not have anadequate fill time. This pattern of late, normal and premature beatsthen continues such that the next beat 490 following beat 480 has anadequate fill time and thus appears much like beat 460. The magnitude ofthese waves matches the expected variation in stroke volume illustratedby ECG R waves 492, 494, 496, 498. Accordingly, upon viewing thispattern, a health care provider will recognize that the variability inPLETH 420 corresponds to the pattern of heart beats shown in ECG 410 andshould therefore dismiss concerns regarding pulsus paradoxus.

FIG. 5 illustrates a wave review comparison of an ECG waveform and PLETHwaveform of the patient from FIG. 4. Wave review 500 shows ECG 502 andPLETH 504 waveforms. ECG 502 demonstrates a wide complex tachycardia,rate 140, without obvious P waves. P waves represent depolarization ofthe atria. PLETH 504 depicts periodic large pulses 508, 510, 512, 514,516, 518 resulting from intermittent sequential atrio-ventricularcontractions, diagnostic of ventricular contractions withatrio-ventricular dissociation. The slower atrial rhythm is not obviouson ECG 502 but the presence of P waves can be discerned by subtleflattening of T waves 520, 524, 528, 532, 536 preceding each of the Rwaves 522, 526, 530, 534, 538, respectively, responsible for the largerplethysmographic excursions (pulses) 508, 510, 512, 514, 516, 518 ofPLETH 504. In this aspect, a health care provide viewing these resultscan see that the variability in PLETH 504 corresponds to the pattern ofheart beats depicted by ECG 502 and therefore the variability is heartbeat by heart beat rather than respiratory thus eliminating pulsusparadoxus as a potential cause of the variability.

It is further appreciated that Traube-Hering-Mayer waves may further beresponsible for variations in a plethysmographic waveform.Traube-Hering-Mayer waves are oscillations that have been measured inassociation with blood pressure, heart rate, cardiac contractility,pulmonary blood flow, cerebral blood flow and movement of thecerebrospinal fluid, and peripheral blood flow including venous volumeand thermal regulation. The waves exhibit a rate typically slightly lessthan and independent of respiration and resemble the primary respiratorymechanism. Thus, where, for example, a fluctuation in pulse pressurewith the frequency of respiration persists after respiration has beenarrested, Traube-Hering-Mayer waves, in addition to respiratory causes,should be considered in evaluating the cause of the fluctuation.

FIG. 6 illustrates a system 600 detecting waveform patterns anddetecting an abnormality in the pattern. In this aspect, system 600includes a database 610 to receive and store data representing aphysiological condition of the patient over a period of time, whereinthe data comprises data corresponding to respiratory activity andcardiac oscillations. The data corresponding to the respiratory activityand cardiac oscillations may be recorded and stored in a storage device612 of database 610. System 600 may include a patient monitoring device620 to generate data representing a physiological condition of a patientover time for storage in database 610. In an alternative embodiment,system 600 may include any number of patient monitoring devices 620connected to a patient to measure, for example, heart rate, bloodvolume, systolic and diastolic blood pressure, and/or plethysmographicoxygen saturation. In this aspect, patient monitoring devices 620include, monitoring devices 620-1 through 620-N. In one embodiment,patient monitoring device 620 may be a plethysmographic oximeter or anysimilar device capable of measuring and displaying respiratory and/orcardiac activity. In an alternative embodiment, patient monitoringdevice 620 may include a plethysmographic oximeter and an ECG (e.g.,monitoring device (620-1) is a plethysmographic oximeter and monitoringdevice (620-2) is an ECG). Data generated by patient monitoring devices620 may be in various formats. In one embodiment, the data may be intext format. In an alternative embodiment, the data may be in a waveformformat suitable for providing a waveform image.

System 600 may include module 630 to receive and process data generatedby monitoring devices 620. In this aspect, module 630 may include awaveform analysis application 640 running on module 630. In oneembodiment, waveform analysis application 640 converts waveform datainto a quantifiable format for measuring changes in waveformcharacteristics. In this aspect, portions of the waveform data areexamined by waveform analysis application 640 to extract features orpatterns that are pertinent to analysis of the waveform data. There area number of techniques that may be used to extract pertinent informationfrom the waveform data. For example, pertinent information from thewaveform data can be extracted by determining frequency and amplitude ofthe waveforms at various points. The waveform data can also be analyzedby examining at least two cycles of the waveform in conjunction with asecond waveform. This may be accomplished by capturing a segment of thewaveform data of each waveform and defining a single cycle and analyzingthe captured segment perhaps by applying a suitable algorithm, such aspattern recognition algorithm or transform algorithm. For example,waveform analysis application 640 may be configured to extract a patternconsistent with a respiratory cycle of a waveform representing cardiacoscillations illustrated in FIG. 3 by examining relevant data accordingto the above processes.

In one embodiment, waveform analysis application 640 may further beconfigured to monitor the waveform patterns and extract abnormalpatterns (e.g. wave oscillations in cardiac waveforms) as described inreference to FIG. 3. When an abnormality in, for example, aplethysmogrhpic waveform is found for several consecutive respiratorycycles, storage of the two signals (respiratory and cardiac) is begun.The signals are then quantified and compared to determine a score aspreviously discussed. If the score is within a range indicative of anabnormality such as pulsus paradoxus, alarm 660 further included inmodule 630 sounds alerting the care provider. Alternatively or inaddition to, information pertinent to the waveform data may be extractedmanually. This may be accomplished by, for example, a person who istrained to recognize pulsus paradoxus by manually examining thephysiological data (including waveform data) displayed on a displaydevice 650 (i.e. interface) of module 630.

In the preceding detailed description, specific embodiments aredescribed. It will, however, be evident that various modifications andchanges may be made thereto without departing from the broader spiritand scope of the claims. The specification and drawings are,accordingly, is to be regarded in an illustrative rather thanrestrictive sense.

1. A system comprising: a database to store data representing aphysiological condition of a patient over a period of time, wherein thedata comprises data corresponding to respiratory activity and datacorresponding to cardiac oscillations, wherein the data corresponding torespiratory activity comprises a first waveform and the datacorresponding to the cardiac oscillations comprises a second waveform;and a module for detecting a pattern of the second waveform consistentwith a cycle of a first waveform and detecting an abnormality indicativeof pulsus paradoxus in the pattern.
 2. The system of claim 1, whereinthe second waveform comprises a plethysmographic waveform and the firstwaveform comprises a respiratory waveform.
 3. The system of claim 1,further comprising: an interface for displaying the first waveform andthe second waveform.
 4. The system of claim 1, wherein each of the firstwaveform and the second waveform data is generated by a pulse oximeter.5. The system of claim 1, wherein the first waveform data and the secondwaveform data is generated from a plurality of monitoring devicescoupled to a patient.
 6. The system of claim 1, wherein the secondwaveform data comprises an electrocardiogram (ECG) waveform.
 7. Thesystem of claim 1, wherein the waveform representing respiratoryactivity is generated from an electrocardiogram (ECG) coupled to apatient.
 8. The system of claim 1, wherein detecting an abnormalityindicative of pulsus paradoxus comprises determining a value from thepattern based on an average offset of the waveform from a baseline andan average amplitude of the waveform.
 9. The system of claim 1, whereinwhen an abnormality is detected an alarm is triggered to alert a healthcare provider of the abnormality.